From 3aee2fd43e3059a699af2b63c6f2395e5a55e515 Mon Sep 17 00:00:00 2001
From: KatolaZ <katolaz@freaknet.org>
Date: Wed, 27 Sep 2017 15:06:31 +0100
Subject: First commit on github -- NetBunch 1.0
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+ <title>hv_net(1) - Sample a random graph with an assigned joint degree distribution</title>
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+<body id='manpage'>
+ <div class='mp' id='man'>
+
+ <div class='man-navigation' style='display:none'>
+ <a href="#NAME">NAME</a>
+ <a href="#SYNOPSIS">SYNOPSIS</a>
+ <a href="#DESCRIPTION">DESCRIPTION</a>
+ <a href="#PARAMETERS">PARAMETERS</a>
+ <a href="#EXAMPLES">EXAMPLES</a>
+ <a href="#SEE-ALSO">SEE ALSO</a>
+ <a href="#REFERENCES">REFERENCES</a>
+ <a href="#AUTHORS">AUTHORS</a>
+ </div>
+
+ <ol class='man-decor man-head man head'>
+ <li class='tl'>hv_net(1)</li>
+ <li class='tc'>www.complex-networks.net</li>
+ <li class='tr'>hv_net(1)</li>
+ </ol>
+
+ <h2 id="NAME">NAME</h2>
+<p class="man-name">
+ <code>hv_net</code> - <span class="man-whatis">Sample a random graph with an assigned joint degree distribution</span>
+</p>
+
+<h2 id="SYNOPSIS">SYNOPSIS</h2>
+
+<p><code>hv_net</code> <var>graph_in</var> [SHOW]</p>
+
+<h2 id="DESCRIPTION">DESCRIPTION</h2>
+
+<p><code>hv_net</code> samples a random graph whose joint degree distribution is
+equal to that of another graph provided as input, using the
+hidden-variable model proposed by Boguna ans Pastor-Satorras.</p>
+
+<h2 id="PARAMETERS">PARAMETERS</h2>
+
+<dl>
+<dt><var>graph_in</var></dt><dd><p> File containing the edge list of the existing graph. If equal to
+ '-' (dash), read the edge list from STDIN.</p></dd>
+<dt class="flush">SHOW</dt><dd><p> If the second parameter is equal to <code>SHOW</code>, the program prints on
+ STDERR the hidden variable and actual degree of each node.</p></dd>
+</dl>
+
+
+<h2 id="EXAMPLES">EXAMPLES</h2>
+
+<p>Let us assume that we want to create a graph whose joint degree
+distribution is equal to that of the graph contained in
+<code>AS-20010316.net</code> (i.e., the graph of the Internet at the AS level in
+March 2001). We can use the command:</p>
+
+<pre><code> $ hv_net AS-20010316.net > AS-20010316.net_rand
+</code></pre>
+
+<p>which will sample a random graph with the same joint-degree
+distribution and will save its edge list in the file
+<code>AS-20010316.net_rand</code> (notice the STDOUT redirection operator
+<code>></code>). Additionally, we can also save the values of the hidden
+variables and actual degrees of the nodes by specifying <code>SHOW</code> as a
+second parameter:</p>
+
+<pre><code> $ hv_net AS-20010316.net SHOW > AS-20010316.net_rand 2>AS-20010316.net_rand_hv
+</code></pre>
+
+<p>In this case, the file <code>AS-20010316.net_rand_hv</code> will contain the
+values of the hidden variable of each node and of the actual degree of
+the node in the sampled graph, in the format:</p>
+
+<pre><code> h1 k1
+ h2 k2
+ ....
+</code></pre>
+
+<h2 id="SEE-ALSO">SEE ALSO</h2>
+
+<p><a class="man-ref" href="conf_model_deg.1.html">conf_model_deg<span class="s">(1)</span></a>, <a class="man-ref" href="conf_model_deg_nocheck.1.html">conf_model_deg_nocheck<span class="s">(1)</span></a></p>
+
+<h2 id="REFERENCES">REFERENCES</h2>
+
+<ul>
+<li><p>M. Boguna and R. Pastor-Satorras. "Class of correlated random
+networks with hidden variables". Phys. Rev. E 68 (2003), 036112.</p></li>
+<li><p>V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
+Methods and Applications", Chapter 7, Cambridge University Press
+(2017)</p></li>
+<li><p>V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
+Methods and Applications", Appendix 14, Cambridge University Press
+(2017)</p></li>
+</ul>
+
+
+<h2 id="AUTHORS">AUTHORS</h2>
+
+<p>(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 <code><v.nicosia@qmul.ac.uk></code>.</p>
+
+
+ <ol class='man-decor man-foot man foot'>
+ <li class='tl'>www.complex-networks.net</li>
+ <li class='tc'>September 2017</li>
+ <li class='tr'>hv_net(1)</li>
+ </ol>
+
+ </div>
+</body>
+</html>
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